SYNTHESIS & INTEGRATION
Analysis of abrupt transitions in ecological systems
BRANDON T. BESTELMEYER,1,† AARON M. ELLISON,2 WILLIAM R. FRASER,3 KRISTEN B. GORMAN,3,4
SALLY J. HOLBROOK,5,8 CHRISTINE M. LANEY,6 MARK D. OHMAN,7 DEBRA P. C. PETERS,1 FINN C. PILLSBURY,1
ANDREW RASSWEILER,8 RUSSELL J. SCHMITT,5,8 AND SAPNA SHARMA9
1
USDA-ARS Jornada Experimental Range, New Mexico State University, MSC 3JER Box 30003,
Las Cruces, New Mexico 88003 USA
2
Harvard Forest, Harvard University, Petersham, Massachusetts 01366 USA
3
Polar Oceans Research Group, Sheridan, Montana 59749 USA
4
Simon Fraser University, Department of Biological Sciences, 8888 University Drive, Burnaby, British Columbia V5A 1S6 Canada
5
Department of Ecology, Evolution and Marine Biology, University of California, Santa Barbara, California 93106 USA
6
Environmental Science and Engineering Program, University of Texas at El Paso, El Paso, Texas 79968 USA
7
Scripps Institution of Oceanography, University of California at San Diego, La Jolla, California 92093 USA
8
Marine Science Institute, University of California, Santa Barbara, California 93106 USA
9
Center for Limnology, University of Wisconsin-Madison, Madison, Wisconsin 53706 USA
Citation: Bestelmeyer, B. T., A. M. Ellison, W. R. Fraser, K. B. Gorman, S. J. Holbrook, C. M. Laney, M. D. Ohman, D. P. C.
Peters, F. C. Pillsbury, A. Rassweiler, R. J. Schmitt, and S. Sharma. 2011. Analysis of abrupt transitions in ecological
systems. Ecosphere 2(12):129. doi:10.1890/ES11-00216.1
Abstract. The occurrence and causes of abrupt transitions, thresholds, or regime shifts between
ecosystem states are of great concern and the likelihood of such transitions is increasing for many
ecological systems. General understanding of abrupt transitions has been advanced by theory, but
hindered by the lack of a common, accessible, and data-driven approach to characterizing them. We apply
such an approach to 30–60 years of data on environmental drivers, biological responses, and associated
evidence from pelagic ocean, coastal benthic, polar marine, and semi-arid grassland ecosystems. Our
analyses revealed one case in which the response (krill abundance) linearly tracked abrupt changes in the
driver (Pacific Decadal Oscillation), but abrupt transitions detected in the three other cases (sea cucumber
abundance, penguin abundance, and black grama grass production) exhibited hysteretic relationships with
drivers (wave intensity, sea-ice duration, and amounts of monsoonal rainfall, respectively) through a
variety of response mechanisms. The use of a common approach across these case studies illustrates that:
the utility of leading indicators is often limited and can depend on the abruptness of a transition relative to
the lifespan of responsive organisms and observation intervals; information on spatiotemporal context is
useful for comparing transitions; and ancillary information from associated experiments and observations
aids interpretation of response-driver relationships. The understanding of abrupt transitions offered by this
approach provides information that can be used to manage state changes and underscores the utility of
long-term observations in multiple sentinel sites across a variety of ecosystems.
Key words: alternative states; Bouteloua eriopoda; desert grassland; krill; leading indicators; Nyctiphanes simplex;
Pachythyone rubra; penguins; Pygoscelis; regime shifts; sea cucumbers; thresholds.
Received 27 July 2011; revised 2 November 2011; accepted 4 November 2011; published 9 December 2011.
Corresponding Editor: Y. Pan.
Copyright: Ó 2011 Bestelmeyer et al. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits restricted use, distribution, and reproduction in any medium, provided the original
author and sources are credited.
† E-mail: bbestelm@nmsu.edu
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The development of management strategies to
mitigate abrupt transitions requires strong linkages among theory, data, and case studies, but
there is little guidance available for using
historical or ongoing studies to detect or respond
to abrupt transitions. There is confusion and
disagreement about what changes constitute
transitions (Rudnick and Davis 2003, Schroder
et al. 2005) and a limited understanding of
ecological mechanisms causing them (Brown
and Archer 1999, Collie et al. 2004). Empiricists
disagree about how to best gather and interpret
relevant data (Petraitis and Latham 1999, Bertness et al. 2002, Schroder et al. 2005), while
theoreticians develop leading indicators of
abrupt transitions that demand large amounts
of data (Carpenter and Brock 2006, Biggs et al.
2009, Contamin and Ellison 2009). There is little
clarity regarding the use of existing data and the
design of future studies to detect and mitigate
undesired state changes (Bestelmeyer 2006,
Groffman et al. 2006).
A common, systematic approach to analyzing
state changes could allow ecologists to marshal a
large body of useful data and detailed knowledge to help society better understand and,
ultimately, manage abrupt transitions. Here, we
illustrate a general, data-based, and mechanismcentered analysis of abrupt transitions using four
datasets from the US Long-Term Ecological
Research (LTER) program on pelagic ocean,
coastal benthic, polar marine, and semi-arid
terrestrial ecosystems. These LTER data include
some of the longest time-series available for both
causal environmental drivers and biological
response variables, and interpretations of associations between the drivers and the response
variables are enhanced by experimental and
mechanistic studies conducted at the same sites.
We first lay out a synthetic framework for
describing abrupt transitions and state changes
that can be used to compare and contrast among
case studies. We then propose a standard
analytical approach that provides strong tests
for detecting abrupt transitions between states.
This approach revealed unexpected results for
the pelagic ocean system for which a ‘‘regime
shift’’ had been described previously, provided
stronger evidence for hypothesized state changes
in the coastal benthic ecosystem, and yielded
new evidence for state changes in the polar
INTRODUCTION
Many ecological systems can exist in two or
more states that differ in abundance or composition of species, rates of ecological processes,
and ecosystem services provided by them (Beisner et al. 2003, Suding et al. 2004). Smooth,
gradual transitions between ecosystem states are
unremarkable, occurring during succession or as
ecosystems track gradually changing environmental conditions. In contrast, abrupt transitions
between ecosystem states are typically unexpected and can have wide-ranging, negative impacts.
Abrupt transitions happen either when the
gradually changing environment passes a critical
point or when discrete perturbations cause
sudden changes in underlying environmental
drivers. Abrupt and irreversible transitions are
forecast to increase as climatic changes and
depletion of natural resources both accelerate
(Millennium Ecosystem Assessment 2005, Fagre
et al. 2009). Such forecasting, however, is difficult
because there are many different causes of state
changes (Hastings and Wysham 2010) and
because existing approaches demand far more
data than are normally available (Carpenter et al.
2011).
Managing state changes is as difficult as
forecasting them. When environmental changes
are not severe, or when organisms with short
lifespans and generation times rapidly track
environmental drivers, some state changes can
be reversed in relatively short periods of time
(50 years) if drivers are returned to pre-change
conditions or perturbations are eliminated (Jones
and Schmitz 2009). In other cases, environmental
change can result in state changes that persist
long after environmental drivers have returned
to earlier conditions. The persistence of these socalled ‘‘ecological thresholds’’, ‘‘regime shifts’’,
‘‘phase shifts’’, or ‘‘catastrophes’’ (Hughes 1994,
Scheffer et al. 2001, Groffman et al. 2006) is
caused by time-lags in the responses of biological
systems to environmental change (hysteresis),
differences in the relationships between state
variables and environmental drivers before and
after the state change, or the appearance of novel
feedbacks among state variables and drivers that
reinforce the new state (Scheffer et al. 2001,
Lindig-Cisneros et al. 2003, Briske et al. 2006,
Suding and Hobbs 2009).
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marine and semi-arid terrestrial ecosystems. Our
analyses illustrate how to identify and interpret
causes of abrupt transitions, and also illustrate
limitations common to many datasets used to
study abrupt transitions and state changes. We
conclude with recommendations for improving
ongoing and nascent long-term research programs aimed at detecting and forecasting state
changes.
State changes in biological responses are
caused directly or indirectly by changes in
environmental drivers. Drivers are usually abiotic and include changes in climate (e.g., temperature, precipitation), or land-use (e.g., resource
extraction, nutrient input rates). Environmental
drivers usually are considered ‘‘slow variables’’
(e.g., Folke et al. 2004, Carpenter and Brock 2006)
because they typically change much more slowly
than biological response variables (Fig. 2A). The
textbook example of a slow environmental driver
leading to a state change is long-term phosphorus input leading to an abrupt shift from
oligotropic (clear blue) to eutrophic (muddy
green) lakes (Carpenter and Brock 2006). Drivers
can also change abruptly, however, with dramatic effects. Triggers (a.k.a. pulse disturbances) are
either abrupt shifts in drivers or singular events,
such as droughts, hurricanes, disease outbreaks,
invasive species introductions, or fire, that
directly affect biological responses (Suding and
Hobbs 2009). State changes often are caused by
interactions among multiple drivers and triggers
(Nystrom et al. 2000, Breshears et al. 2005).
Whereas drivers are typically presented as time
series concurrent with biological responses (Fig.
2A), triggers are discrete events in time or
relatively short, discrete sections of a time series
(e.g., an El Niño period, Holmgren et al. 2006).
Biological responses (a.k.a. response variables
or state variables) are used to recognize alternative states (Mantua 2004, Schroder et al. 2005,
Andersen et al. 2009). Response variables are
especially important because they usually can be
measured or monitored easily, and persistent
changes in their mean or increases in their
variance are used as indicators of state changes.
Like environmental drivers, biological response
variables typically are represented as time series
of the abundance or biomass of individual
species or suites of trophically similar species
(Daskalov et al. 2007; Fig. 2A).
Response mechanisms describe how drivers
and triggers interact and affect responses (Fig. 1).
Of particular importance are changes in relationships between drivers and responses caused by
positive feedbacks between them that amplify
changes in both drivers and responses and
reinforce alternative states (Rietkerk et al. 2004).
Positive feedbacks often involve complex chains
of interactions involving biological and physical
A common framework for describing state change
Studies across a wide range of ecosystems
reveal five common data elements used in the
recognition and analyses of state change: environmental drivers; triggers; biological responses;
response mechanisms; and contextual information (Fig. 1). We introduce these element categories based on earlier syntheses (Scheffer et al.
2001, Andersen et al. 2009, Suding and Hobbs
2009) and consideration of the datasets presented
herein.
Fig. 1. A conceptual model of the relationships
between the elements of abrupt transitions and
analytical approaches used to investigate them.
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Fig. 2. Three classes of driver-response relationships and analytical indicators of transitions and state changes.
The top row (A) illustrates time series of driver and response variables in linear tracking, threshold, and
hysteretic systems. The second row (B) illustrates that the frequency distribution of the observations should shift
from unimodal to bimodal when a threshold or hysteretic change occurs. The third row (C) illustrates how one
leading indicator, the variance of the time series, should differ among the three classes of driver-response
relationships. As the transition becomes more abrupt and the post-transition state becomes more distinctive from
the pre-transition state, the variance should become more peaked at the transition point. The bottom row (D)
illustrates changes in the driver-response relationships from linear (in the linear tracking class) to nonlinear (in
the threshold class) to hysteretic.
processes, including Allee effects (BourbeauLemieux et al. 2011), trophic cascades (Carpenter
et al. 1999, Carpenter et al. 2011), habitat
fragmentation and extinction cascades (Swift
and Hannon 2010, He and Hubbell 2011), land
surface-climate feedbacks (Foley et al. 2003, Cook
et al. 2009), or spreading desertification (Peters et
al. 2004). Data on response mechanisms are
derived most frequently from manipulative
experiments, natural history observations, and
expert knowledge (Choy et al. 2009).
Finally, contextual information documents
characteristics of the environmental setting that
can influence driver-response relationships and
that can vary among case studies. For example,
lake morphometry (Genkai-Kato and Carpenter
2005), stream channel geometry (Heffernan et al.
2008), soil texture (Bestelmeyer et al. 2006), and
distance to source populations (Hughes et al.
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1999) result in spatial variation in biological
responses to drivers and triggers. Similarly, the
timing of disturbance events with respect to
seasonal period can determine their effects on
biological responses (Nystrom et al. 2000).
Understanding spatiotemporal context can help
to reconcile differences among case studies
illustrating general types of transitions and state
changes (e.g., Petraitis et al. 2009). Contextual
information also can help translate scientific
analyses into meaningful policy recommendations and management interventions (Carpenter
et al. 2011).
An approach for identifying abrupt transitions
and state changes in ecological systems
Three general classes of mechanisms are
postulated to produce abrupt transitions: linear
tracking, threshold response, and hysteresis
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(following Scheffer et al. 2001, Andersen et al.
2009, Suding and Hobbs 2009) (Fig. 2). Note that
all three mechanisms can yield patterns that have
been referred to as ‘‘thresholds’’ in biological
response data. An integration of exploratory data
analysis, time-series analysis, and linear or nonlinear modeling (see Methods) provide evidence
for assigning each case to a class.
The distinction between linear tracking and
threshold responses is whether: the distribution
of the biological response variable is unimodal
vs. weakly bimodal (Fig. 2B); the variance in the
biological response is constant vs. increasing
slightly as the environment changes or a trigger
occurs (Fig. 2C); and the relationship between
the environmental driver and the biological
response is linear vs. nonlinear (Fig. 2D).
Following the terminology of preceding authors,
both linear tracking and threshold responses can
be reversed; as the driver returns to its initial
(pre-change) value, environmental conditions
and biological response variables often track
them with at most short time-lags. Note that
the threshold in ‘‘threshold response’’ refers to
the nonlinear biological response to a change in
driver magnitude, rather than irreversibility.
In contrast to threshold responses, hysteretic
responses result from persistent environmental
changes, changes in feedbacks between drivers
and response variables, or long time lags in
biological responses to drivers. In such systems,
even if the environmental driver returns to earlier
values, the biological response may not return to
its earlier state, or does so only slowly, at a
markedly different magnitude of the driver, or
along a different path from the one it took to
reach its new state (Fig. 2D). The functional form
of the relationship between environmental driver(s) and biological response(s) typically differs
before and after a state change.
Peninsula (Palmer Station LTER; http://pal.
lternet.edu), and Chihuahuan Desert (Jornada
Basin LTER; http://jornada-www.nmsu.edu). The
California Current System study focused on the
abundance of a euphausiid (krill), Nyctiphanes
simplex, as a biological response and its relationship to the Pacific Decadal Oscillation Index
(PDO) as a primary environmental driver; PDO
represents changes in the ocean physical environment that affect krill populations, including
advection patterns and water column conditions.
The Southern California Rocky Reef study
focused on the abundance of the red sea
cucumber (Pachythyone rubra) and its relationship
to the number of days with large waves (.3.25
m) per year. These large waves disrupt the
dominance of P. rubra and allow dominance of
macroalgae and associated fauna. The Western
Antarctic Peninsula study considered shifts in the
abundances of three Pygoscelis penguin species:
the Adélie (P. adeliae), chinstrap (P. antarctica),
and gentoo (P. papua). These biological responses
were considered with respect to changes in the
seasonal duration of sea-ice that influences the
foraging and breeding biology of these species.
Finally, the Chihuahuan Desert study examined
changes in the production of the dominant grass
species black grama (Bouteloua eriopoda) and its
relationship to summer rainfall that governs its
production. Details on each case study can be
found in the Appendix.
General analytical approach
For each of the four case studies described
individually below, we used a sequence of five
steps to identify abrupt transitions and characterize state changes with respect to the classes of
mechanisms: (1) visualization of temporal patterns in drivers and response variables; (2)
locating and statistically testing one or more
breakpoints in time-series of response variables;
(3) statistical testing of unimodality of frequency
distributions of response variables; (4) calculation
of temporal variance (a leading indicator used to
forecast state transitions) of response variables,
and (5) assessment of relationships between
response variables and drivers before and after
breakpoints identified in (2). Contextual information used to interpret the results was derived from
ancillary experimental data, expert knowledge on
triggers and response mechanisms, and other
METHODS
Case studies
We examined long-term datasets from four USLTER programs to characterize abrupt transitions
and state changes following our framework; the
California Current System (California Current
Ecosystem LTER; http://cce.lternet.edu), Southern California Rocky Reef (Santa Barbara Coastal
LTER; http://sbc.lternet.edu), Western Antarctic
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natural history information (Appendix). Datasets
and associated metadata are archived on, and
publicly available from, the Harvard Forest Data
Archive (http://harvardforest.fas.harvard.edu/
data/archive.html), dataset HF170. All data
manipulation and statistical analyses were performed using base and user-contributed functions
in the R language environment (R Development
Core Team 2011), as detailed below. The R code
used is presented in the Supplement and also
available with dataset HF170 described above.
Prior to any analyses, observations of response
variables were standardized: zi ¼ (xi x̄ )/sd(x).
By working in standard deviation units, data and
analyses were comparable across the studies. The
response and driver variables were unique to
each of the four case studies (Figs. 3–6) and time
series ranged from 28 to 59 years long. The time
series of the responses in each case study
included missing data, so modeled values were
used in place of missing values. Modeled values
were generated from a normal distribution with
the mean and variance equal to the running
mean and variance, respectively, of the standardized measured values bracketing the missing
value(s). For example, in a time series running
from 1970–2010, if observations were missing for
1975–1978 and 1980, the modeled values would
be sampled from N(mean[z1974, z1979, z1981],
SD[z1974, z1979, z1981]). Below, we use fzig to refer
to the time series that includes both observed and
modeled response variables in standard deviation units.
Temporal patterns in responses were visualized by fitting a locally weighted scatterplot
smoother (LOESS) (Cleveland and Devlin 1988)
to fzig. The smoothed curve was fit using the
loess function in the R stats library. Default
settings were used: a weighted least-squares fit to
a fraction of the points in a moving window that
spanned three-quarters of the points. The weighting function for each point was proportional to
the cube of the distance to each point in the
moving window. The curve is fit using a lowdegree polynomial to a subset of the data using a
weighted least squares method (Cleveland and
Devlin 1988).
Breakpoints in fzig were identified using the
strucchange package (Zeileis et al. 2002). First,
the time series was detrended by differencing
using the diff function in the R base library. A
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detrended time series of standardized observations has slope equal to zero, and if there is no
breakpoint in the time series, the intercept also
would be equal to zero. Breakpoints are years
after which the intercept of the detrended time
series changes significantly, and detection of one
or more breakpoints would suggest that an
abrupt transition may have occurred. A combination of three approaches was used to detect
breakpoints and to determine the number of
breakpoints in the fzig for each case study. First, a
cumulative sum (CUSUM) plot summarized the
cumulative sums of differences between each
value and the overall mean. A breakpoint was
indicated by a sudden change in direction of the
CUSUM plot. Because CUSUM plots are ‘‘jagged’’ and can indicate many directional changes,
residual sums of squares (RSS) and the Bayesian
Information Criterion (BIC) were used to identify
the number of breakpoints that significantly
improved the fit of the CUSUM model (Zeileis
et al. 2002). Finally, we examined the statistical
significance of each breakpoint identified from
RSS and BIC using an F statistic (based on the
Chow test statistic, Zeileis et al. 2002). CUSUM,
RSS, and BIC plots all indicated either one or two
breakpoints in each of the case studies. Because
changes in response variables exceeded two
standard-deviation units only in the case of the
gentoo penguins, however, F statistics were
significant only at the a ¼ 0.1 level.
Histograms and density smoothers of fzig
were plotted to determine if the frequency
distribution was unimodal or bimodal. Departures from unimodality were tested using Hartigan’s dip test (Hartigan and Hartigan 1985) as
implemented in the dip function in the R diptest
library. This test is very conservative—the distribution of the test statistic is based on asymptotic
and empirical samples relative to a uniform
distribution. A table of quantiles (P-values) is
provided in the file qDiptab in the R diptest
library. The power of the test (for a ¼ 0.05) is 80%
when sample size ¼ 50; since our sample sizes
(excluding missing values) ranged from 27 to 55,
we accepted P-values 0.10 as statistically
significant evidence for departure from unimodality. The linear tracking model should yield
a unimodal distribution of fzig, whereas a
threshold or hysteresis model should yield a
bimodal distribution of fzig (see Fig. 2).
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Fig. 3. Results of analyses for the California Current System. In the time series of the biological response (A), the
observed data are shown as points scaled in standard deviation units, the time series (which include modeled values)
as grey lines connecting the points, and the locally weighted regression (loess) illustrating the temporal pattern as a
solid black curve. Breakpoints identified using CUSUM, RSS, and BIC are identified with arrows on the x-axis. The
potential alternate state begins the year after the breakpoint. In the time series of the driver (B), the observed data are
shown as points and the time series as black lines connecting the points. There are no missing values in the time series
of drivers in Figs. 3–6. The frequency distributions (C) are equivalently scaled across Figs. 3–6, and all bins are the
same width (0.5 SD units). The probability density function of the observations is overlain on the histogram. Similarly,
the time series of variance (D) are all equivalently scaled across Figs. 3–6, and breakpoints again are identified.
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Fig. 4. Results of analyses for the Southern California Rocky Reef (A–E), following the same order and rules as
for Fig. 3. For the relationship between the driver and response (E), only data from the first and second states are
shown.
(continuation of Fig. 3 legend). Finally, the relationship between the driver and response (E) are illustrated for the
initial state (solid symbols, black lines) and post-transition state (open symbols, grey lines). In this figure only, the
data from the third state are combined with those from the first state (negative phase of the PDO).
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Fig. 5. Results of analyses for the Western Antarctic Peninsula (A–E), following the same order and rules as for
Fig. 3. The three species of penguins are illustrated in three colors (Adélie penguins in black, chinstrap penguins
in orange, gentoo penguins in blue). The relationship between the driver and response is shown only for Adélies
because there are too few data for the other species.
Changes in temporal variance of fzig were
of abrupt transitions in the hysteresis model
assessed because abrupt increases in variance
(Carpenter and Brock 2006). We calculated
have been demonstrated to be a leading indicator
changes in temporal variance of the differenced
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Fig. 6. Results of analyses for the northern Chihuahuan Desert (A–E), following the same order and rules as for
Fig. 3. The two pastures are illustrated in black (Pasture 2) and orange (Pasture 9).
time series using the rollapply function in the R
zoo library. The window size used for each case
variance for years prior to the onset of our
moving window could not be calculated (as the
study was the shortest time-interval between
breakpoints in the time series; window sizes
number of points available was less than the
window size); we indicate those years with
ranged from seven to 30 years. The temporal
dashed lines in Figs. 3D, 4D, 5D, and 6D. We
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note that using temporal variance as a leading
indicator works best for very long time series
(50 observations) of temporally autocorrelated
data sampled at high frequency. Real ecological
data, such as those analyzed here, are of
relatively short duration (,50 observations),
and ecologists generally attempt to minimize
temporal autocorrelation by sampling less frequently. If the threshold response occurs within
the lifespan of the organism, but sampling
frequency is on the same time-scale as organism
lifespan or generation time (cf. Fig. 7), a state
change or threshold response may not be
detected. Finally, if observation errors are relatively large or if multiple linear and non-linear
processes interact and mute the response variables, changes in variance may not be detected
even though state changes have occurred
(Scheffer et al. 2009, Brock and Carpenter 2010,
Carpenter et al. 2011).
Finally, relationships between response and
driver variables were examined for the data
overall and for data partitioned into before and
after breakpoints. For the California Current
System data, the data were partitioned into sets
when the PDO was either negative (before the
first breakpoint and after the second breakpoint)
or positive (in between the two breakpoints). For
the Southern California Rocky Reef data, we only
examined the data before the first breakpoint and
after the first, but before the second, breakpoint
(only three values for the driver variable were
available after the second breakpoint). For the
Western Antarctic Peninsula data, we only
examined the data for Adélie penguins, because
there were too few data for chinstrap or gentoo
penguins after their 2004 breakpoints. We used
linear (lm) and non-linear (nls) regression in the
R stats library to model the relationships between
responses and drivers. The expectation for the
linear tracking model was that there would be
similar response-driver relationships before and
after the breakpoint(s), and the expectation for
the hysteresis model was that there would be
different response-driver relationships before
and after the breakpoint(s). For example, a
different slope and intercept for a linear regression fitting response-driver relationships or a
non-linear versus linear fit for data and after the
identified breakpoint would support the hysteresis model (Scheffer and Carpenter 2003, Bai et
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Fig. 7. The time series of the biological responses
from each case study rescaled to the maximum life
span of each organism. Maximum lifespan was used to
ensure complete population turnover. Each tick on the
x-axis indicates one lifespan (value in parentheses).
al. 2010).
RESULTS
A pelagic ocean ecosystem:
The California Current System
Data collected within the California Current
System (CCS) provide an example of abrupt
transitions with a linear tracking mechanism
(Fig. 3). The CCS includes a major coastal
upwelling biome that extends from British
Columbia to Baja California. A variety of
directional changes in the ocean environment
(including rising sea level, oceanic warming,
increased density stratification, decreased transparency, acidification, and changes in hypoxia)
may be affecting planktonic populations and the
pelagic food web. There are also important
sources of interannual (e.g., El Niño-Southern
Oscillation [ENSO]) and decadal (e.g., Pacific
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Decadal Oscillation [PDO]) (Mantua et al. 1997)
variability in this ecosystem. Long-term variations in krill abundance are correlated with the
PDO (Brinton and Townsend 2003) and time
series of N. simplex abundance display abrupt
shifts from one persistent state to another, which
may imply hysteresis (Fig. 2) and/or a positive
feedback mechanism (deYoung et al. 2008). We
assessed the evidence for alternative states in the
krill population in the southern sector of the
California Current where temperate-subarctic,
cool-water zooplankton fauna enter from the
north, and subtropical, warm-water fauna, including N. simplex (Brinton et al. 1999), enter
from the south. This geographic location is
therefore likely to be sensitive to changes in
large-scale ocean circulation patterns incorporated into the PDO.
The six-decade California Cooperative Oceanic
Fisheries Investigations (CalCOFI) record revealed that N. simplex generally was rare when
the PDO was in the negative phase (anomalously
cool waters in the NE Pacific). Abrupt changes of
the PDO from negative to positive were tracked
by increases in N. simplex abundance and vice
versa (Fig. 3A, B). Strong El Niño (1958–60, 1997–
98) and La Niña (1998–99) events had positive
and negative influences, respectively, on N.
simplex abundance that interacted with changes
in the PDO to accentuate abrupt changes or
interrupt relationships with PDO (Appendix).
Although the warm phase between 1977–1998
was a period of consistently high abundance of
N. simplex relative to the time period before and
after, our data indicate that contrary to previous
work (e.g., deYoung et al. 2008) this should not
be considered a different ecological ‘‘regime’’ and
hysteresis is not indicated. The frequency distribution of abundances were unimodal and, most
definitively, the relationship between N. simplex
abundance (response variable) and the PDO
(driver) varied linearly with the variations in
the PDO and was identical in both the warm and
the cool phases of the PDO. Thus, the California
Current System illustrates a case of linear
tracking (Hsieh and Ohman 2006), without
discrete, definable (or ‘‘preferred’’) system states.
Such linear tracking may be common in shortlived organisms that can quickly and closely
track abrupt changes in drivers.
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A coastal benthic ecosystem:
The Southern California Rocky Reef
Data from shallow rocky reefs off the coast of
Southern California provide evidence of hysteresis due to predation-mediated feedbacks (Fig.
4). The reefs can support either a macroalgaedominated community or one characterized by
high densities (.10,000/m2) of the filter-feeding
sea cucumber, P. rubra. Spatially extensive sea
cucumber-dominated states can persist for decades and dramatically alter reef food webs
(Rassweiler et al. 2008, Rassweiler et al. 2010).
The loss of macroalgae leads to a reduction in
micro-crustaceans and their associated fish predators (Holbrook and Schmitt 1989, Schmitt and
Holbrook 1990a, b).
Time-series data from nine sites spread along a
5-km stretch of coastline on the north shore of
Santa Cruz Island illustrate the mechanisms of
abrupt sea cucumber-to-macroalgae transitions
(Fig. 4A; see also Rassweiler et al. 2010). The
frequency distribution of annual sea cucumber
abundance data revealed evidence of bimodality
(Fig. 4C). The first transition from macroalgae to
sea cucumber dominance occurred in the late
1980s and was associated with a series of years in
which there were few high wave events during
winter storms (Fig. 4B). High waves dislodge sea
cucumbers from algal beds (Rassweiler et al.
2008), but when winter storms are weak, sea
cucumbers competitively displace algae by
smothering and killing them.
Frequent, strong storms returned after 1995,
but the relationship between days of high waves
(driver) and sea cucumber abundance (response)
disappeared and sea cucumbers continued to
dominate the system (Fig. 4E). Consumption of
algal spores by abundant sea cucumbers allowed
this species to persist in the face of increased
wave disturbance (Rassweiler et al. 2008). This
relationship switched to yet another low cucumber state when predatory sea stars colonized the
system in late 2002 (Appendix). Thus, this case
conforms to a hysteresis model in which stabilizing feedbacks conferred resilience with respect
to the environmental driver.
A polar marine ecosystem:
The Western Antarctic Peninsula
The Western Antarctic Peninsula (WAP) provides another example of hysteresis due to the
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effects of multiple, interacting drivers (Fig. 5).
Since 1950, annual mean air temperature in some
regions has increased by 28C, and winter air
temperature has increased by nearly 68C (Smith
et al. 1996, Vaughan et al. 2003, Turner et al.
2006) These climatic changes have caused longterm reductions in the regional extent and
duration of winter sea-ice (Smith and Stammerjohn 2001, Stammerjohn et al. 2008a, Stammerjohn et al. 2008b), a proximate driver of
directional environmental change in the WAP
marine ecosystem (Smith et al. 2003, Ducklow et
al. 2007, Moline et al. 2008). One important
change is a poleward shift in breeding ranges of
three closely related penguin species; the Adélie,
chinstrap, and gentoo (Forcada et al. 2006,
Ducklow et al. 2007, Forcada and Trathan
2009). There is considerable debate regarding
the environmental drivers of change in penguin
breeding population dynamics (Patterson et al.
2003, Forcada and Trathan 2009, Trivelpiece et al.
2011).
Nearly 40 years of data collected from the
Palmer Archipelago near Anvers Island, Antarctica (Appendix: Fig. A2) illustrate abrupt declines
in the Adélie penguin breeding population
beginning in 1993, and abrupt increases in
numbers of breeding chinstrap and gentoo
penguins beginning in 2004 (Fig. 5A). Bimodality
of annual abundance data was not clearly
evident (Fig. 5C), nor did temporal variance
(Fig. 5D) illustrate dramatic changes before or
during the observed population changes. However, analysis of the relationship between the
proximate driver (sea-ice duration; Fig. 5B) and
Adélie penguin breeding population size revealed that prior to the 1993 breakpoint, the
abundance of this species was essentially unresponsive to variation in sea-ice duration, however after 1993 these variables were strongly and
positively correlated (Fig. 5E), conforming to the
hysteresis model. We did not examine driverresponse relationships for chinstrap or gentoo
penguins because only five data points on yearly
numbers of breeding pairs have been obtained
since the 2004 breakpoint. Progressive climate
warming resulted in an abrupt transition operating through multiple, cascading ecological drivers and feedbacks, including reduced sea-ice
duration, changes in terrestrial snowfall accumulation that affect penguin breeding biology, and
v www.esajournals.org
feedbacks between Adélie population reductions
and predator efficiency (Appendix).
A semi-arid grassland ecosystem:
The Chihuahuan Desert
Data from northern Chihuahuan Desert grasslands provide an example of hysteresis involving
a strong trigger and novel feedbacks (Fig. 6).
These grasslands were dominated historically by
black grama grass (Bouteloua eriopoda), but during
the last 150 years, black grama grasslands have
shifted to shrublands dominated by xerophytic
woody plants. Similar shifts from grasslands to
shrublands have occurred in semi-arid systems
throughout the world (Archer 1995). Historically,
black grama grass persisted through episodic
droughts, and shrub cover within black grama
grasslands was limited by competition for water,
limited shrub seed dispersal, and possibly
periodic fire (Peters and Gibbens 2006). Heavy
cattle grazing on black grama grass during
drought periods is believed to have initiated the
grassland-to-shrubland transition (Appendix). It
has not been clear, however, how rapidly the
initial grassland loss takes place and therefore
how best to employ monitoring strategies to
prevent it (Bestelmeyer 2006).
Time-series data on annual production of black
grama grass collected during the mid-1900s from
two pastures in the Jornada Experimental Range
near Las Cruces, NM, USA, indicate the start of
an abrupt transition in 1948. In that year, there
was no black grama production (Fig. 6A), and
this lack of production coincided with the onset
of a prolonged drought (Fig. 6B). Several lines of
evidence suggest that this system conforms to the
hysteresis model. First, annual production was
bimodal (Fig. 6C), indicating two alternative
states. Second, black grama production exhibited
an increase in temporal variance during the
transition (Fig. 6D) associated with a period of
low and variable growing-season (July–September) rainfall (Fig. 6B). Third, driver-response
regressions show that prior to 1948, black grama
production had a positive relationship to growing-season precipitation (Nelson 1934). After
1948, however, this relationship weakened and
overall production was low regardless of growing-season rainfall (Fig. 6E). The shift in black
grama production was very abrupt, never attaining previous high values after 1950. A positive
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BESTELMEYER ET AL.
feedback between soil erosion and low grass
cover appears to have precluded grassland
recovery after a return to higher levels of
precipitation (Appendix).
To summarize, we used a common approach
to determine if and when abrupt transitions
occurred, to evaluate leading indicators that
could forecast the transitions, and to match each
case to the appropriate class of mechanisms
(Figs. 3–6). The timing of abrupt transitions was
successfully identified in all four cases. An
increase in variance that could serve as a leading
indicator was observed only in the Chihuahuan
Desert case due to the extreme interannual
fluctuation preceding grassland collapse. The
linear tracking model was indicated for the
California Current case due to unimodality in
the distribution of biological response values and
linearity in the driver-response relationship. The
hysteresis model was indicated in the other three
cases due to varying combinations of evidence,
including the strong nonlinearity in the driverresponse relationship for the West Antarctic
Peninsula, and both bimodality of biological
responses and nonlinear driver-response relationships in the Southern California Rocky Reef
and Chihuahuan Desert cases. In all four cases,
plausible response mechanisms supported the
classification of the case to the general mechanism.
transitions and alternative states, examination of
frequency distributions of response variables,
consideration of variance patterns used in forecasting, and analysis of patterns and mechanisms
of driver-response relationships can be applied to
many of these datasets.
A common, systematic approach applied
across different datasets will advance a general
understanding of abrupt transitions and state
changes. Such a common approach is especially
important now, as abrupt, often irreversible
transitions are forecast to increase as climatic
change accelerates (Millennium Ecosystem Assessment 2005, Fagre et al. 2009); a coherent,
integrated strategy is needed to manage and
mitigate the expected state changes. Our comparative exploration of case studies suggests
some lessons for future analyses of existing data
and guidance for new observation and monitoring networks embarking on long-term studies.
Leading indicators may have limited utility
Mathematical modeling (Scheffer et al. 2009)
and empirical studies conducted in temperate
lake ecosystems (Carpenter et al. 2011) suggest
that increased variance in the time series of
biological responses can be used to forecast
abrupt transitions. Many systems, however,
may show no change or even decreases in
variance (Hastings and Wysham 2010). Our
analysis of four different systems, three of which
showed clear hysteretic patterns between drivers
and responses, suggest that this leading indicator
must be carefully scaled to the time-scale of
dynamics in the biological response variable
(e.g., organism lifespan; Fig. 7). Short-lived
organisms can track abrupt changes in drivers
closely. Thus, some transitions, such as those in
the California Current krill (Fig. 3), may appear
abrupt until rescaled to the short lifespan of this
organism (Fig. 7). In contrast, especially when
lifespan matches the dominant time scale of
environmental variability (Hsieh and Ohman
2006), other transitions may appear gradual,
but actually occur quite abruptly relative to the
organism’s lifespan (e.g., penguins: Fig. 5A; black
grama grass: Fig. 6A).
To be informative, leading indicators of rising
variance require many highly autocorrelated
samples collected within the lifespan of the
sentinel organism of interest. The traditional
DISCUSSION
A common approach
These case studies illustrate that abrupt transitions and state changes not only can be
identified, but also can be understood via a suite
of general concepts (Fig. 1) and relatively simple
methods. Although the availability of longrunning time series of both drivers and responses
has been limited (Andersen et al. 2009, Carpenter
et al. 2011), long-term data now can be accessed
from LTER and related sites (http://ecotrends.
info), and many institutions worldwide are
investing considerable resources establishing
new ecological observation networks (e.g., National Ecological Observatory Network, Global
Lake Ecological Observatory Network, Ocean
Observatory Initiative, Paleoecological Observatory Network). The sequence of methods used
here, including an objective evaluation of abrupt
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ecological emphasis on temporal trend as opposed to variance has led researchers to undervalue the importance of regularly sampled time
series and fine temporal intervals. Detection of
abrupt transitions and state changes require time
series without missing values (or ones that can be
reasonably modeled). If sampling designs capture weakly- or un-correlated measures of
abundance, or if studied organisms are longlived and transitions occur rapidly (i.e., between
samples), measures of temporal variance may not
be informative. In such cases, it would be
worthwhile to identify faster-changing variables
(Carpenter et al. 2011) that reflect organismal
performance in populations, such as physiological status. However, if transitions are not caused
by ‘‘slow’’ variables but instead are caused by
abrupt, unpredictable triggers acting within
vulnerable systems, variance-based leading indicators may provide only limited information
(Hastings and Wysham 2010). In such cases,
mean values of a slow variable might be used to
signal heightened susceptibility to abrupt transition given a trigger, but the transition itself may
not be predictable.
namics. The location of the California Current
System and Western Antarctic Peninsula studies
relative to biogeographic transition zones, the
Chihuahuan Desert study on sandy soils during
a period of comparatively high grazing pressure,
and the Southern California Rocky Reef study
area relative to the shifting southern range limit
of sea-stars each influenced the patterns observed
in their respective time-series of biological
responses (Appendix). Data on the same response variables collected at other locations or
times might yield different results or reveal how
large-scale forces such as ocean circulation,
regional climate, physiography, or soils mediate
abrupt transitions (Rietkerk et al. 2004, Williams
et al. 2011). As case studies of abrupt transitions
accumulate, researchers should ensure that spatial and temporal measurement scales of drivers
and response variables are recorded and are
appropriately congruent. Researchers should
also be alert for changes in context when
comparing studies.
Multiple lines of evidence aid interpretation
Different kinds of state changes were identified
by different analyses. Interpretations of state
changes were greatly facilitated by consideration
of data and observations apart from the driver
and response time series. The choice of the
‘‘right’’ driver and the ‘‘right’’ response variable
was based on detailed short- and long-term
experiments along with ancillary information
and anecdotal observations that provided important clues to the interpretation of time-series data.
In spite of the case-specific nature of response
mechanisms, we predict that a systematic review
of additional cases will reveal a limited set of
classes of interactions between drivers, triggers,
and responses (cf. Fig. 2). This framework can
guide future investigations and promote a datasupported understanding of abrupt transitions
(Walker and Meyers 2004).
Driver-response relationships are powerful tools
Researchers should hesitate to infer response
mechanisms based solely on the presence of
threshold patterns in biological response variables; analysis of driver-response relationships
provide stronger tests of such inferences. For
example, the linear tracking model (Fig. 2) may
appear to have abrupt transitions when biological responses track abrupt changes in drivers, as
in the California Current System (Fig. 3A). Such
observations have been used to suggest the
existence of alternative stable states (deYoung et
al. 2008) that is not supported by our analysis
(Fig. 3E). Driver-response relationships can then
be explained with regard to specific response
mechanisms (e.g., grass cover loss leading to soil
erosion feedbacks in the Chihuahuan Desert case;
Appendix).
Can we manage state changes?
Long-term, multi-faceted case studies and
datasets can provide retrospective explanations
of transitions and state changes for specific cases,
but can they provide useful information for
proactive management? We suggest that a body
of transition datasets representing different kinds
of ecosystems would provide several useful
Context is critical
In the four cases that we examined, the
historical context and the location in which the
study was conducted relative to physical processes occurring at larger spatial scales both had
important consequences for the observed dyv www.esajournals.org
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BESTELMEYER ET AL.
Santa Barbara (06-20276), the University of Wisconsin
(08-22700), an NSF contract to the LTER Network
Office at the University of New Mexico (08-32652), and
the Natural Sciences and Engineering Research Council of Canada (NSERC). The LTER EcoTrends project,
the LTER Network Office, and Laurie Chiasson at
Harvard Forest supported the data synthesis efforts.
The California Current System data were collected in
part with the assistance of A. Townsend (Scripps
Institution of Oceanography Pelagic Invertebrates
Collection) and the late E. Brinton. Collection of the
Southern California Rocky Reef data prior to 2000 was
supported by NSF awards to RJS and SJH. The Jornada
Experimental Range data collection and synthesis was
supported by staff of the USDA Agricultural Research
Service. The authors thank Ben Baiser, Audrey BarkerPlotkin, Elizabeth Crone, Elizabeth Farnsworth, David
Foster, Brian Hall, Dave Orwig, Sydne Record, Kristina
Stinson, and Jack Williams for helpful discussions and
comments on early versions of the manuscript.
insights for managing state changes. First, they
would suggest the appropriate drivers, triggers,
and biological response variables to be emphasized in monitoring, as well as the spatial and
temporal design elements needed for detection.
Second, depending upon the drivers, such
analyses would indicate useful strategies for
managing the effects of drivers. In some cases,
such as when response variables are affected by
broad-scale physical drivers (e.g., California
Current System) direct control is not feasible
but forecasting and adaptation approaches could
be developed. Third, case studies can be used to
evaluate the abruptness of transitions, particularly with respect to organisms’ lifespans (Fig. 7).
Cases of high abruptness, such as in the semiarid grasslands and Antarctic peninsula, suggest
that management reactions to indicators of
transition must be especially rapid (e.g., adjust
stocking rates in drought periods and establish
institutions that can respond rapidly) (MezeHausken et al. 2009). Finally, case studies can be
used to indicate the potential utility of early
warning indicators to forecast transitions given
their abruptness and the feasibility of measuring
the appropriate attributes with sufficient temporal and/or spatial resolution. Many abrupt
transitions may ultimately need to be managed
according to a precautionary principle that
acknowledges our limited ability to develop
indicators of imminent transition or to respond
rapidly enough to such indicators (Contamin and
Ellison 2009). In those situations, case studies
might indicate simple values in driver or
biological response levels that are related to the
likelihood of abrupt transition. To make informed choices among the wide range of possible
strategies for detecting and managing abrupt
transitions, ecologists and policymakers must
commit to sustaining, renewing, and initiating
observational platforms in multiple sentinel sites.
The resulting data can, as we have shown,
produce useful maps for navigating our changing world.
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SUPPLEMENTAL MATERIAL
APPENDIX
affect population growth of N. simplex.
The primary breeding center of N. simplex is
located off Baja California (Brinton et al. 1999), to
the south of our study site. Abrupt increases in
advection of organisms into our region from the
south, reflecting individuals introduced from a
population showing regular recruitment in a
favorable habitat, would have rapidly increased
the proportion of younger individuals (calyptopis larvae) in the population. Conversely, if
conditions changed favorably in situ, without
corresponding changes in advection, the contribution of calyptopis larvae would have increased
more gradually over time. Finally, if in situ
conditions for N. simplex deteriorated without a
corresponding change in advection, there should
have been a decline in relative abundance of
larval stages due to reduced egg production by
adults.
Inspection of Fig. A1 relative to the two abrupt
transitions identified in the population time
series (Fig. 3A) suggested that altered advection
was the more plausible hypothesis. The 1976–77
abrupt increase in total abundance of N. simplex
was accompanied by a simultaneous increase in
the proportion of larvae, without a temporally
lagged response. The explanatory power of the
PDO for the temporal variability in N. simplex is
corroborated by modeling variations in N.
simplex as an autoregressive (AR-1) process
related only to present and one previous state
of the PDO, which shows excellent agreement
with observations.
Furthermore, the rapid decline in total abundance in 1999–2000 was not accompanied by a
gradual diminution in contribution by larvae.
Detail on the California Current System case study
To assess the likely mechanism underlying the
relationship between variations in the California
Current euphausiid (krill) Nyctiphanes simplex
and the Pacific Decadal Oscillation (PDO) first
shown by Brinton and Townsend (2003) and reexamined in this paper (Figs. 3A–E), we analyzed the life-history structure of N. simplex from
1951 to 2009. Zooplankton were sampled in the
upper 210 m or 140 m of the water column using
0.5-mm mesh plankton nets (Ohman and Smith
1995) and analyzed by E. Brinton. The stations
used in the analysis were from Southern California (Lavaniegos and Ohman 2007); the
California Cooperative Oceanic Fisheries Investigations (CalCOFI) lines 80 through 93, from
shore to station 70, springtime cruises, and nighttime samples only. This station pattern differs
slightly from that used by Brinton and Townsend
(2003). Annual averages of the Pacific Decadal
Oscillation (Mantua et al. 1997) were obtained
from the monthly values posted at: http://jisao.
washington.edu/pdo/PDO.latest.
Counts of krill during these 48 years were
available for four life-history stages: calyptopis,
furcilia, juveniles, and adults. The proportional
composition of each stage (Fig. A1) was used to
differentiate between two primary mechanisms
by which changes in the physical environment,
as represented by the PDO, might have influenced euphausiid abundance: altered advection
of organisms into or out of the study region, and
altered in situ changes in water column conditions (e.g., temperature, food, predators) that can
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Detail on the Southern California Rocky Reef
case study
We used data on abundance of red sea
cucumbers (Pachythyone rubra) from nine sites
on the north shore of Santa Cruz Island, CA. Sites
were situated along a 5 km stretch of coastline
(centered on 34.058N, 119.7378W), with six sites
established in 1982 and three more added in 1989
(for a complete description of the sites see
Schmitt and Holbrook 1986, Holbrook and
Schmitt 1989). Sampling was usually annual
between September and November; in some
instances not all sites were sampled every year.
Sites were similar in depth, slope, exposure and
initial benthic community structure. At each site
there were two fixed 40 m transects, one each
along the 6-m and 9-m isobath. The percent cover
of P. rubra, understory macroalgae (mainly
species in the genera Eisenia, Laurencia, Gelidium,
Rhodymenia, Codium and Corallina), sea urchins,
and Macrocystis pyrifera were estimated using
point-contact methods (eight randomly located
points per meter per transect). The sea-star
Pycnopodia helianthoides was counted in a 2-m
wide swath centered on each transect. Abundance of P. rubra was estimated as the average
percent cover across all transects sampled in each
year. Similarly, abundance of P. helianthoides was
estimated as the mean density across all transects
sampled in each year.
Data on wave heights were taken from buoys
operated by the National Oceanic and Atmospheric Administration (www.ndbc.noaa.gov).
Because no single buoy operated without interruption between 1982 and 2008, data from three
buoys were combined. The East Santa Barbara
buoy (No. 46053) is located nearest to the sites
where organismal cover and abundance data
were collected (23 km NNW of the study sites)
and, therefore, wave heights recorded at this
buoy were used when available. On days when
data were not available from the East Santa
Barbara buoy, data from the Point Arguello and
Santa Maria buoys (Nos. 46023 and 46011, ;135
km NW of the monitoring sites) were used to
estimate wave height and water temperature in
the east channel. Estimates were made based on
linear regressions developed from days when all
three buoys were operational (Rassweiler et al.
2008).
To quantify wave intensity, we calculated the
Fig. A1. Time series of life-history stages (calyptopis,
furcilia, juveniles, and adults) of the euphausiid
Nyctiphanes simplex from the Southern California sector
of the California Current System. Illustrated are the
proportions each life-history stage of total N. simplex
springtime abundance, night-time samples only, averaged over the region sampled.
Rather, the proportion of larvae remained roughly constant, although production was intermittent. Hence, we conclude that the predominant
mechanism underlying rapid changes in the
euphausiid-PDO time series was the introduction
or flushing out of individuals through altered
transport. Once introduced into the study site, N.
simplex were able to reproduce and survive for
extended periods of time because of more
favorable conditions in situ, but the rapid
increases/decreases in abundance were initiated
by altered advection. Further support for this
interpretation comes from observations of responses by N. simplex to major El Niño events.
These events typically have resulted only in
transient increases in abundance of N. simplex
(Fig. 3A, B; see also Brinton and Townsend 2003)
initiated by transport from the south. The
unusual El Niño of 2009–2010 was not accompanied by changes in N. simplex abundance because
this particular event propagated through atmospheric teleconnections rather than through
altered ocean advection (Todd et al. 2011).
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number of days each year when maximum
significant wave height exceeded 3.25 m. October
1 was the cut-off between years, because biological sampling typically occurred near this date.
We chose 3.25 m as our definition of a large
storm because previous experiments revealed
that the competitive effects of algae on P. rubra
abundance only occurred when waves exceeded
this height (Rassweiler et al. 2008, Rassweiler et
al. 2010). We did not include wave data from the
summer period of each year (May through
September), because summer swells typically
come from a southerly direction and the northern
shores of the Channel Islands, where our sites are
located, are sheltered from these waves.
One challenge in analyzing state changes is
that there can be more than two states. In this
case study, an exclusive focus on P. rubra initially
suggested only two states: prior to 1987 the sea
cucumbers were nearly absent, from 1987 to 2002
they were very abundant although variable, and
after 2002 they returned to their low density
state, approaching the densities initially observed
in the early 1980s (Fig. 4A, E). However, when
other invertebrates were considered, it became
apparent that the post-2002 low abundance state
was not equivalent to the pre-1987 state. Rather,
the post-2002 system consists of different species
and is maintained by a new mechanism—
predation. The third state was initiated by the
sudden arrival of the predatory sea star, P.
helianthoides, a mobile and voracious predator,
with well-established potential to decimate echinoderm populations (Mauzey et al. 1968, Pearse
and Hines 1987). In 2003 when sea stars first
became abundant they were typically large—35
cm or more across—suggesting that they immigrated into these sites, either from deeper water
or from the western end of the island where they
have been previously observed (Eckert 2007).
The role of sea stars in the initiation and
maintenance of the third state illustrates that an
interaction between multiple environmental factors triggered the post-1987 shift into the high P.
rubra state. The absence of predators alone was
not sufficient to have caused this shift; before
1987, P. rubra was rare even though predators
were absent. Similarly, it is unlikely that low
waves could have triggered a shift into the high
density state if P. helianthoides had been present,
because the sea stars exert strong top-down
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control on P. rubra abundance. Our results
underscore the complex nature of state changes.
Different mechanisms can initiate, maintain, or
end a state, and interactions between multiple
drivers may be necessary to trigger shifts in
states.
Detail on the Western Antarctic Peninsula
case study
Species comprising polar marine systems have
evolved life histories associated with the presence or absence of sea-ice, often broadly termed
sea-ice obligate or sea-ice intolerant species,
respectively (Ducklow et al. 2007, Moline et al.
2008, Siniff et al. 2008). Pygoscelis penguins of the
Western Antarctic Peninsula (WAP, Fig. A2)
integrate environmental variability over large
spatio-temporal scales due to their longevity
and spatially extensive foraging (Fraser and
Trivelpiece 1996). Relationships between environmental drivers and penguin population dynamics (Fig. 5A, B, E) reflect life history
integration of this variability, and the abundance
and distribution of these species provided some
of the earliest evidence of rapid climate-induced
change in the WAP (Fraser et al. 1992, Woehler et
al. 2001, Forcada et al. 2006, Siniff et al. 2008,
Forcada and Trathan 2009, Gorman et al. 2010,
Trathan et al. 2011, Trivelpiece et al. 2011).
Physical oceanographic processes occurring
along the WAP are important proximate drivers
of changes in regional climatology (Thompson
and Solomon 2002, Marshall et al. 2004, Ducklow
et al. 2007, Martinson et al. 2008, Meredith et al.
2008). Interactions between climate phases and
physical oceanography has resulted in displacement of the cold, dry polar climate that historically dominated the region by a warm, moist
sub-Antarctic system characteristic of the northern WAP and Scotia Arc (Ducklow et al. 2007).
Penguin population data in this case study
span nearly four decades, a period during which
sea-ice extent decreased by 50% and sea-ice
duration decreased by 85 days (Smith et al.
2003, Stammerjohn et al. 2008b). Number of
breeding pairs of Pygoscelis penguins has been
estimated annually since the mid-1970s from
surveys of nesting individuals on seven islands
within 15 km of Palmer Station, a US scientific
research station located on Anvers Island (Fig.
A2). Most of the data used in these analyses were
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Fig. A2. The Western Antarctica Peninsula (WAP). Lower left box shows the WAP relative to other regions of
the Antarctic. Upper right box shows the Palmer LTER study area near Anvers Island. Data from penguin
colonies used in these analyses are located on islands within the Palmer Archipelago that are shaded in yellow.
The location of Palmer Station is shaded in gray. Image generated from base maps provided by the National
Snow and Ice Data Center’s map server A-CAP (http://nsidc.org/agdc/acap/).
based on numbers obtained immediately following peak clutch completion (November–December). In the few years where this peak was missed
due to weather and sea-ice conditions hindering
island access, the next survey conducted closest
to this period was used. During 1980, 1984, 1985
and 1988, regional totals were estimated from
partial surveys (i.e., data from islands not
surveyed were estimated based on percent
increases or decreases on adjacent islands that
were surveyed). Analyses to examine relationships between sea-ice duration and Adélie
penguin population response were lagged by
four years to account for delayed reproductive
maturity of these species (Ainley 2002), however,
results were qualitatively similar for lags equal to
zero and five. Following (Stammerjohn et al.
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2008b), annual sea-ice duration was based on the
number of days that elapsed between the first
day of advance and the first day of retreat for the
Palmer LTER study region near Anvers Island
(Fig. A2); an ‘‘ice year’’ begins in mid-February of
year y and ended in mid-February of year y þ 1.
Since 1975, the breeding population of the true
Antarctic, sea-ice obligate, Adélie penguin (Ainley 2002) along the Palmer Archipelago has
declined by 85% (Fig. 5A). The breakpoint in
Adélie population dynamics occurred in 1993
(Fig. 5A); this response is temporally consistent,
given the species lag in reproductive maturity,
with the poorest sea-ice conditions evident in the
remote sensing record (Fraser and Hofmann
2003, Stammerjohn et al. 2008b) and the lowest
abundance of Antarctic krill (Euphausia superba)
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in the contemporary WAP record (Fraser and
Hofmann 2003) that occurred in 1990. Krill is the
current dominant prey not only of Adélie
penguins along the Palmer Archipelago, but also
of sub-Antarctic, sea-ice-intolerant chinstrap and
gentoo penguins (cf. Fraser et al. 1992, Williams
1995, Fraser and Hofmann 2003, Trivelpiece et al.
2011) whose breeding populations increased
dramatically beginning in 2004. Although it has
been hypothesized that krill abundance is a
primary driver of the population dynamics of
all three Pygoscelis species (Forcada and Trathan
2009, Trivelpiece et al. 2011), their contrasting
population dynamics along the Palmer Archipelago do not support this general hypothesis. For
example, chinstrap and gentoo penguins established local founder colonies in 1975 and 1993,
respectively (Fig. 5A). Although several lines of
evidence suggest that krill has important nutritional impacts on reproduction and survival of
these penguins, these impacts, both positive and
negative, are ultimately mediated by speciesspecific life history affinities to sea-ice (Fraser et
al. 1992, Ducklow et al. 2007, Forcada and
Trathan 2009).
An additional key environmental driver in this
system appears to be increased snowfall due to
escalating oceanic venting of moisture resulting
from reduced winter sea-ice conditions (Thompson et al. 1994, Fraser and Patterson 1997,
Patterson et al. 2003, Massom et al. 2006). This
increased snowfall affects penguin demography
via two response mechanisms. First, heavy
spring snow eventually floods nests and drowns
chicks (Fraser and Patterson 1997, Patterson et al.
2003). Adélie penguins are particularly vulnerable to flooding because their breeding phenology
is highly synchronized, and they initiate egg
production earlier than the other Pygoscelis
species, when snow accumulations peak (Williams 1995, Massom et al. 2006). In contrast,
gentoo penguins have a much more plastic
breeding phenology, and along with chinstraps,
typically breed 3–4 weeks later than Adélie
penguins (Williams 1995, Ducklow et al. 2007).
Second, brown skuas (Catharacta lonnbergi ),
territorial avian predators, prey on penguin eggs
and chicks. As the size of Adélie colonies declines
within skua territories due to snowfall, penguins
become progressively more vulnerable to skua
depredation. Once colonies have decreased to
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;50 breeding pairs, skuas appear to locally
extirpate these colonies by annually consuming
all penguin eggs and chicks (Fraser and Patterson
1997, Patterson et al. 2003). As with the sea
cucumbers and sea stars, different mechanisms
can initiate, maintain, or end penguin population
states.
Detail on the Chihuahuan Desert case study
This case pertains to the sandy soils (typic
aridic, thermic, coarse-loamy Calcids, Cambids,
and Argids) of southern New Mexico, west Texas
(USA) and northern Chihuahua states (Mexico),
where mean annual precipitation is ;250–350
mm. In these areas, state changes from grasslands to shrublands are among the best recognized of terrestrial transitions (Scheffer and
Carpenter 2003), but there is considerable debate
about their underlying causes and timing. A
combination of overgrazing and drought is
thought to have caused the transition by shifting
the interaction of competition and physical
processes in favor of shrubland (i.e., the so-called
teeter-totter model of Schlesinger et al. 1990).
This simple model belies a more complex, multistaged process that we have recently revealed by
analyzing historical and long-term data. This
evolving ‘‘multi-stage model’’ breaks up the
grassland-shrubland transition into a series of
stages, including: loss of dominant grass cover in
discrete areas (stage I); invasion or expansion of
shrubs within low-grass cover conditions (stage
II); and lateral expansion of soil erosion, grassland loss, and expansion of shrub populations
(stage III) (Peters et al. 2006, Bestelmeyer et al.
2011).
Black grama grass (Bouteloua eriopoda) initially
is the dominant plant and ground cover (.60%),
with very few or no shrubs. It persists in the face
of periodic droughts, grows and sets seed
reliably, stabilizes surface soil horizons, and
may competitively exclude shrubs (Herbel and
Gibbens 1996). In contrast, other perennial grass
species have lower canopy cover, die out during
droughts, and coexist with shrubs (Nelson 1934,
Herbel et al. 1972, Gibbens and Beck 1987,
Herrick et al. 2002).
Data for this case study were obtained from
annual reports archived between 1938 and 1972
at the Jornada Experimental Range. Production
of black grama grass (lbs/acre) was extracted
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BESTELMEYER ET AL.
(i.e., more than 100% of the 35% recommended
use) indicated overgrazing. Precipitation data
were from the West Well rain gauge of the USDA
Rain Gauge Network (http://usda-ars.nmsu.edu/
data_long-term-datasets.html), which lay at the
southwest and northwest corners of pastures 2
and 9, respectively. We paired growth year black
grama grass production (measured in fall) with
the monsoonal rainfall totals (July–September) of
that same year. Known limitations of the data
include: (1) a lack of precise spatial relationships
between production, patchy rainfall, and pasture
utilization; and (2) complex relationships between intra- and interannual rainfall and plant
production that are not reflected in the data
(Schwinning and Sala 2004, Yahdjian and Sala
2006).
The state change data reflect the loss of black
grama grass cover in discrete areas (i.e., stage I of
the ‘‘multi-stage model’’). The observed state
change was initiated by severe drought years
occurring during a period with intermittently
high levels of utilization (cattle grazing; Fig. A3).
Years of very low summer (July–September)
rainfall led to years of relatively low black grama
production (Fig. 6A, B, E). The failure to reduce
livestock numbers during those years led to
overgrazing, and successive years of overgrazing
(measured as high utilization values) were
followed by years of reduced black grama grass
production (Fig. A3). That dry, extremely windy
conditions occurring in low grass cover conditions could initiate extensive, severe soil erosion
and subsequent collapse of black grama grassland became widely appreciated in the early
1950s (see also Okin et al. 2006). These data,
however, reveal how rapidly (over 2 years) these
effects can lead to persistent reductions in black
grama.
Fig. A3. The time series of black grama grass
(Bouteloua eriopoda) production plotted along with
utilization for one pasture (Pasture 2). Pasture 9
exhibited a similar pattern. Note the high utilization
in 1951, coinciding with the onset of extensive soil
erosion noted in the Jornada monthly report in
December 1951.
from tables in these reports for pastures 2 and 9,
which were dominated by this species. Estimates
were based on an annually varying number of
15-m long 3 10-cm wide transects. Transects
were added until the standard error of the
estimate was within 10% of mean production
value. On each transect, 100 plants were measured and the height of grazed and ungrazed
tillers was recorded. Standing crop of different
perennial grass species was estimated by clipping all aboveground grass parts, air-drying
them, and weighing them (Jornada Forage Crop
Report, 1942, Jornada archives). Areas of each
pasture that were not dominated by black grama
grass (due to variation in soils) were excluded
from sampling. A utilization scale (Lommasson
and Jensen 1943) was used to estimate the
percent of grazing use for each species, which
was averaged over hundreds of plants (Fig. A3).
Utilization values equaled the percentage of the
recommended biomass removed (35% at that
time), and were determined from a ‘‘large
number of transects’’ randomly placed throughout each pasture in each year; values over 100%
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SUPPLEMENT
Annotated R code used for analyses of abrupt
transitions (Ecological Archives C002-011-S1).
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December 2011 v Volume 2(12) v Article 129